Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques.

This paper presents a novel method for determining the fundamental frequency and modeling slender masonry structures, which is particularly relevant in the context of structural dynamics. This research evaluates the effectiveness of machine learning techniques, specifically artificial neural networks (ANN), and regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNLR), in predicting the fundamental frequency of structures, considering both their geometrical and mechanical properties. The objective is to provide a comprehensive comparison of these methods, emphasizing their benefits, limitations, and potential applications in structural health monitoring. The ANN model demonstrated superior performance to linear and non linear regression methods. The findings could significantly impact both theoretical understanding and practical applications in this field. Moreover, this work paves the way for future research, potentially leading to the creation of more accurate and efficient predictive models for slender masonry structures. This methodology ensures a thorough understanding of the model's behavior and its responsiveness to variations in input parameters. • Comprehensive Database: Robust database of slender masonry structures' characteristics. • Empirical Formulations: New empirical formulas for predicting slender structure frequencies. • ANN Model: ANN model for frequency prediction of slender masonry structures. • Parametric Analysis: Study on geometrical/mechanical parameters' impact on frequency. • Future Research: Groundwork for refining models, ANN identified as the precise model. [ABSTRACT FROM AUTHOR]